Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning

Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yield unreliable results. Advances in deep learning have opened up new avenues for automated measurements in fetal ultrasound, demonstrating human-level performance in various fetal ultrasound tasks. Nevertheless, the majority of these studies are retrospective in silico studies, with a limited number including African patients in their datasets. In this study we developed and prospectively assessed the performance of deep learning models for end-to-end automation of fetal biometry and amniotic fluid volume measurements. These models were trained using a newly constructed database of 172,293 de-identified Moroccan fetal ultrasound images, supplemented with publicly available datasets. the models were then tested on prospectively acquired video clips from 172 pregnant people forming a consecutive series gathered at four healthcare centers in Morocco. Our results demonstrate that the 95% limits of agreement between the models and practitioners for the studied measurements were narrower than the reported intra- and inter-observer variability among expert human sonographers for all the parameters under study. This means that these models could be deployed in clinical conditions, to alleviate time-consuming, repetitive tasks, and make fetal ultrasound more accessible in limited-resource environments.

STARD stands for "Standards for Reporting Diagnostic accuracy studies".This list of items was developed to contribute to the completeness and transparency of reporting of diagnostic accuracy studies.Authors can use the list to write informative study reports.Editors and peer-reviewers can use it to evaluate whether the information has been included in manuscripts submitted for publication.

EXPLANATION
A diagnostic accuracy study evaluates the ability of one or more medical tests to correctly classify study participants as having a target condition.This can be a disease, a disease stage, response or benefit from therapy, or an event or condition in the future.A medical test can be an imaging procedure, a laboratory test, elements from history and physical examination, a combination of these, or any other method for collecting information about the current health status of a patient.
The test whose accuracy is evaluated is called index test.A study can evaluate the accuracy of one or more index tests.Evaluating the ability of a medical test to correctly classify patients is typically done by comparing the distribution of the index test results with those of the reference standard.The reference standard is the best available method for establishing the presence or absence of the target condition.An accuracy study can rely on one or more reference standards.
If test results are categorized as either positive or negative, the cross tabulation of the index test results against those of the reference standard can be used to estimate the sensitivity of the index test (the proportion of participants with the target condition who have a positive index test), and its specificity (the proportion without the target condition who have a negative index test).From this cross tabulation (sometimes referred to as the contingency or "2x2" table), several other accuracy statistics can be estimated, such as the positive and negative predictive values of the test.Confidence intervals around estimates of accuracy can then be calculated to quantify the statistical precision of the measurements.
If the index test results can take more than two values, categorization of test results as positive or negative requires a test positivity cut-off.When multiple such cut-offs can be defined, authors can report a receiver operating characteristic (ROC) curve which graphically represents the combination of sensitivity and specificity for each possible test positivity cut-off.The area under the ROC curve informs in a single numerical value about the overall diagnostic accuracy of the index test.
The intended use of a medical test can be diagnosis, screening, staging, monitoring, surveillance, prediction or prognosis.The clinical role of a test explains its position relative to existing tests in the clinical pathway.A replacement test, for example, replaces an existing test.A triage test is used before an existing test; an add-on test is used after an existing test.
Besides diagnostic accuracy, several other outcomes and statistics may be relevant in the evaluation of medical tests.Medical tests can also be used to classify patients for purposes other than diagnosis, such as staging or prognosis.The STARD list was not developed for these other outcomes, statistics, and study types, although most STARD items would still apply.

DEVELOPMENT
This STARD list was released in 2015.The 30 items were identified by an international expert group of methodologists, researchers, and editors.The guiding principle in the development of STARD was to select items that, when reported, would help readers to judge the potential for bias in the study, to appraise the applicability of the study findings and the validity of conclusions and recommendations.The list represents an update of the first version, which was published in 2003.
More information can be found on http://www.equator-network.org/reporting-guidelines/stard.

Table 1 :
Best-performing deep learning models reported in the state-of-the-art literature for the classification and segmentation tasks to detect 2D standard planes, and assess fetal biometry or amniotic fluid volume.The two last rows present the results of our models, and the last column reports the main contributions.Whether data collection was planned before the index test and reference standard were performed (prospective study) or after (retrospective study) On what basis potentially eligible participants were identified (such as symptoms, results from previous tests, inclusion in registry) How indeterminate index test or reference standard results were handled 9 16 How missing data on the index test and reference standard were handled 9 17 Any analyses of variability in diagnostic accuracy, distinguishing pre-specified from exploratory 10 18 Intended sample size and how it was determined INTRODUCTION3 Scientific and clinical background, including the intended use and clinical role of the index test